Abstract
This paper investigates how norm emergence can be facilitated by agents’ adaptive learning behaviors in networked multiagent systems. A general learning framework is proposed, in which agents can dynamically adapt their learning behaviors through social learning of their individual learning experience. Extensive verification of the proposed framework is conducted in a variety of situations, using comprehensive evaluation criteria of efficiency, effectiveness and efficacy. Experimental results show that the adaptive learning framework is robust and efficient for evolving stable norms among agents.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant 61502072, Fundamental Research Funds for the Central Universities of China under Grant DUT14RC(3)064, and Post-Doctoral Science Foundation of China under Grants 2014M561229 and 2015T80251.
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Yu, C., Lv, H., Sen, S., Ren, F., Tan, G. (2016). Adaptive Learning for Efficient Emergence of Social Norms in Networked Multiagent Systems. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_68
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DOI: https://doi.org/10.1007/978-3-319-42911-3_68
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